A manufacturing management system for an electric vehicle door handle assembly

Abstract

The application discloses an electric vehicle door handle accessory manufacturing management system, specifically relates to the field of intelligent manufacturing, and is used for solving the production chain stability and efficiency problems caused by single insertion task disturbance. The application is characterized in that a scheduling dependency graph is constructed to accurately identify the influence range and conflict intensity of the single insertion task, the production chain coupling constraint is converted into a quantum optimization model for global calculation, and process collapse caused by traditional priority coverage is avoided. The anti-disturbance capability is evaluated based on the process topological characteristics, the fragile link is locked for local optimization, and the overall chain reconstruction calculation overhead is reduced. Digital twinning is used for real-time verification and calibration, the equipment load, material supply and process stability are balanced, the work-in-process backlog and equipment idle risk are reduced. The single insertion task and stable scheduling are fused through time window elastic adjustment, the production rhythm continuity is maintained, the emergency order response speed is improved, the manufacturing system resilience is enhanced, the manual intervention and trial-and-error cost are reduced, and the reliable delivery of multi-variety orders and resource efficiency are ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of intelligent manufacturing, and more particularly, to a door handle accessory manufacturing management system for electric vehicles. BACKGROUND

[0002] In the scenario of automobile parts manufacturing, when the main plant needs to add door handle orders in a very short time due to emergency events such as product recall, the traditional production scheduling system uses a priority override strategy to forcibly insert the production queue. This strategy only places the urgent order at the top of the queue, but does not synchronize the reconstruction of the resource constraint relationship and the subsequent task timing logic of the associated process, resulting in the internal coordination of the scheduling system being destroyed. The root cause lies in that the existing scheduling engine is designed based on the linear superposition assumption, that is, it is believed that simple priority adjustment of independent processes will not cause systemic chain reactions. However, in the actual production chain, there are strong coupling relationships among device capacity, material supply, process parameters and other multi-dimensional constraints, and local disturbance will cause nonlinear propagation through the inter-process dependency network.

[0003] When the urgent order occupies the key device resource, the subsequent tasks in the original scheduling plan that depend on the device will form a backlog due to waiting for resource release, while the upstream processes continue to produce semi-finished products due to scheduling conflicts, and the downstream processes cannot consume in time due to resource mismatch, thereby causing exponential accumulation of work-in-process inventory. On the other hand, the system fragments the non-urgent orders for cutting to catch up with the progress, resulting in frequent switching between idle waiting and overloading of the device, further exacerbating the drift of processing precision and energy loss. This systemic instability caused by out-of-control scheduling disturbance propagation makes the manufacturing execution system fall into a chaotic state for several days, ultimately resulting in a sharp decline in on-time delivery rate, deterioration of quality consistency, and a cliff-like drop in production resource utilization.

[0004] In order to solve the above problems, a technical solution is provided. SUMMARY

[0005] In order to overcome the above-mentioned defects of the prior art, embodiments of the present application provide an electric vehicle door handle accessory manufacturing management system, which accurately identifies the influence range and conflict intensity of the inserted order task by constructing a scheduling dependency graph, converts the coupling constraints of the production chain into a quantum optimization model for global calculation, effectively avoiding the process chain collapse caused by the traditional priority coverage strategy; based on the process topology characteristics, the anti-disturbance ability is directionally evaluated and the vulnerable link is locked to perform local re-optimization, which greatly reduces the calculation overhead of the whole chain reconstruction, and at the same time, through digital twinning real-time verification and dynamic calibration, it ensures that the scheduling scheme reaches a balance between equipment load, material supply and process stability, significantly reducing the work-in-process backlog and equipment idle risk caused by inserted orders; finally, the gradual fusion of inserted order tasks and steady-state scheduling is realized by means of elastic time window adjustment, which maintains the continuity of production rhythm while improving the response speed of emergency orders, overall enhances the resilience of the manufacturing system to respond to emergencies, reduces the frequency of manual intervention and trial-and-error costs, and ensures the reliable delivery of multi-variety orders and resource utilization efficiency, to solve the problems raised in the above background art.

[0006] To achieve the above object, the present application provides the following technical scheme:

[0007] An electric vehicle door handle accessory manufacturing management system, comprising:

[0008] A scheduling graph analysis unit, a conversion optimization solving unit, an analysis priority adjustment unit and a digital twin simulation unit;

[0009] The scheduling graph analysis unit: analyzes the production chain scheduling dependency graph, identifies the equipment nodes and dependent process chains directly associated with the inserted order task, and quantifies the influence radius parameter and conflict intensity coefficient of the inserted order task using a directed graph traversal algorithm;

[0010] The conversion optimization solving unit: converts the influence radius parameter and conflict intensity coefficient into the coupling weight matrix of the QUBO model, and solves the global optimal solution of the equipment time period allocation scheme and the logistics path planning scheme in parallel through a quantum annealing algorithm;

[0011] The analysis priority adjustment unit: based on the process chain topology relationship of the global optimal solution, analyzes the shared equipment proportion and disturbance propagation range between processes, calculates the anti-disturbance ability evaluation value through a preset decay function, and if the evaluation value is lower than the set threshold, resets the equipment priority of the process chain with the deepest propagation range and the most shared equipment, and reconstructs the logistics path to generate a re-optimized scheme;

[0012] The digital twin simulation unit: imports the re-optimized scheme into the digital twin, injects the equipment running state and material arrival data, and if the load balance or the yield rate does not meet the standard, dynamically adjusts the constraint weight of the QUBO model and iteratively optimizes, and at the same time, adjusts the time window elasticity coefficient of non-critical processes according to the process execution progress.

[0013] In a preferred embodiment, the processing procedure of the parsing scheduling graph unit is as follows:

[0014] A production chain scheduling dependency graph is constructed, the production chain scheduling dependency graph is a directed graph, the node set includes process nodes and device nodes, and the edge set represents the use relationship of the process and the device and the dependency relationship between processes; by extracting the device nodes related to the inserted task process from the production chain scheduling dependency graph, the device nodes directly associated with the inserted task are identified; by reverse traversal of the production chain scheduling dependency graph, all processes on which the inserted task process depends are determined, and the dependency process chain of the inserted task is identified; a directed graph traversal algorithm is used to calculate the influence radius parameter of the inserted task; and a directed graph traversal algorithm is used to calculate the conflict intensity coefficient of the inserted task.

[0015] In a preferred embodiment, the processing procedure of the parsing scheduling graph unit further includes:

[0016] The influence radius parameter is the maximum propagation depth of the inserted task in the production chain scheduling dependency graph; and the conflict intensity coefficient is the maximum time overlap degree of the inserted task and the existing scheduling task in the use of the device.

[0017] In a preferred embodiment, the processing procedure of the conversion optimization solving unit is as follows:

[0018] The influence radius parameter and the conflict intensity coefficient are associated with binary variables of device time period allocation and binary variables of logistics path planning respectively, and the interference degree of the inserted task on the existing production plan is calculated; the influence radius parameter is directly used to act on a single binary variable through a linear weight, and the coupling effect of the conflict intensity coefficient between binary variables is represented through a quadratic weight, to form a coupling weight matrix; the coupling weight matrix is input into a quantum annealing machine, and a global optimal solution of the device time period allocation scheme and the logistics path planning scheme is solved in parallel by using a quantum annealing algorithm.

[0019] In a preferred embodiment, the processing procedure of the conversion optimization solving unit further includes:

[0020] The device time period allocation scheme determines the occupancy state of each device in each time period, and the logistics path planning scheme determines the optimized path of material transportation.

[0021] In a preferred embodiment, the processing procedure of the analysis priority adjustment unit is as follows:

[0022] After obtaining the global optimal solution provided by the quantum annealing algorithm, the device time period allocation scheme and the logistics path planning scheme contained therein are extracted, and a process chain topological relationship graph is constructed, in which nodes represent specific processes in the production chain, and edges represent the dependency relationship and device sharing relationship between processes; the dependency relationship edge reflects that the execution of one process must wait for the completion of another process, and the device sharing relationship edge represents that two processes use the same device at the same time period.

[0023] In a preferred embodiment, the processing process of the analysis priority adjustment unit further includes:

[0024] According to the dependency edge and the shared device edge in the process chain topological relationship graph, the shared device proportion of each pair of processes is calculated, and the shared device proportion is defined as the ratio of the number of shared devices of two processes to the total number of devices used by each process;

[0025] Through the breadth-first search algorithm, the disturbance propagation range of each process is determined by traversing the reachable process nodes in the process chain topological relationship graph from each process, and the disturbance propagation range is defined as the number of reachable processes;

[0026] The preset attenuation function is used to calculate the anti-disturbance ability evaluation value of the process chain, and the preset attenuation function is an exponential attenuation function based on the topological distance between processes, and the anti-disturbance ability evaluation value is defined as the sum of the product of the shared device proportion of each process in the process chain and the attenuation factor within the disturbance propagation range of the process;

[0027] When the anti-disturbance ability evaluation value is lower than the preset threshold, the process chain with the largest disturbance propagation range and the most shared devices is identified, and the device priority reset and logistics path reconstruction are performed on it,

[0028] The reset device time period allocation scheme and the reconstructed logistics path planning scheme are replaced with the corresponding parts in the global optimal solution to generate a re-optimization scheme.

[0029] In a preferred embodiment, the processing process of the analysis priority adjustment unit further includes:

[0030] The device priority reset is defined as preferentially allocating devices that are not occupied by other processes or devices with the least occupation time period, and the logistics path reconstruction is defined as selecting the path with the shortest transportation time.

[0031] In a preferred embodiment, the processing process of the digital twin simulation unit is as follows:

[0032] Step one, map the equipment time period allocation scheme and the logistics path planning scheme to the equipment scheduling module and the material transportation module of the digital twin, generate the time allocation table of the virtual equipment and the path planning of the virtual logistics network; collect various data of the equipment through the sensor and the logistics tracking system, and inject the digital twin to simulate the real-time operation of the production chain;

[0033] Step two, calculate the equipment load rate and the complete set rate based on the simulation results, and judge whether the load balancing index and the complete set rate index meet the preset requirements;

[0034] Step three, if the load balancing index or the complete set rate index does not meet the preset requirements, adjust the equipment conflict penalty weight and the logistics cost weight of the QUBO model, and reconstruct the coupling weight matrix, solve the new equipment time period allocation scheme and the logistics path planning scheme through the quantum annealing algorithm, and import the digital twin for iterative optimization;

[0035] Step four, simultaneously identify non-critical processes according to the process execution progress, and adjust the time window elasticity coefficient of the non-critical processes through the fuzzy logic controller;

[0036] Repeat steps one to four until the load balancing index and the complete set rate index meet the preset requirements.

[0037] In a preferred embodiment, the processing process of the digital twin simulation unit further comprises:

[0038] The non-critical process refers to a process with a certain slack time in the production chain, that is, the difference between the earliest start time and the latest start time of the process is greater than the processing time of the process; the calculation method of the slack time is to first determine the earliest start time and the latest start time of the process, then calculate the difference between the two, and compare it with the processing time, if the difference is greater than the processing time, the corresponding process is a non-critical process.

[0039] The technical effects and advantages of the electric vehicle door handle accessory manufacturing management system are as follows:

[0040] The application accurately identifies the influence range and conflict intensity of the inserted task by constructing a scheduling dependency graph, converts the coupling constraints of the production chain into a quantum optimization model for global calculation, effectively avoiding the process chain collapse caused by the traditional priority coverage strategy; based on the topological characteristics of the process, the anti-disturbance ability is directionally evaluated and the vulnerable link is locked for local re-optimization, which greatly reduces the calculation overhead of full-chain reconstruction, and at the same time, through digital twinning real-time verification and dynamic calibration, the scheduling scheme is balanced among equipment load, material supply and process stability, significantly reducing the work-in-process backlog and equipment idle risk caused by inserted orders; finally, the gradual fusion of inserted tasks and steady-state scheduling is realized by means of elastic time window adjustment, which maintains the continuity of production rhythm while improving the response speed of emergency orders, overall enhances the resilience of the manufacturing system to respond to emergencies, reduces the frequency of manual intervention and trial-and-error cost, and ensures the reliable delivery and resource utilization efficiency of multi-variety orders. BRIEF DESCRIPTION OF DRAWINGS

[0041] Figure 1 It is a structural schematic diagram of the manufacturing management system of the door handle accessory for the electric vehicle. DETAILED DESCRIPTION

[0042] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, not all embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the application.

[0043] Embodiment 1: Figure 1 The application provides a manufacturing management system of a door handle accessory for an electric vehicle, comprising: a parsing scheduling graph unit, a conversion optimization solving unit, an analysis priority adjustment unit and a digital twin simulation unit;

[0044] The parsing scheduling graph unit: parses the production chain scheduling dependency graph, identifies the equipment nodes and dependent process chains directly associated with the inserted task, and quantifies the influence radius parameter and conflict intensity coefficient of the inserted task by using a directed graph traversal algorithm;

[0045] The conversion optimization solving unit: converts the influence radius parameter and conflict intensity coefficient into the coupling weight matrix of the QUBO model, and solves the global optimal solution of the equipment time period allocation scheme and the logistics path planning scheme in parallel through a quantum annealing algorithm.

[0046] Analysis priority adjustment unit: based on the global optimal solution of the process chain topology relationship, analyze the proportion of shared equipment between processes and the propagation range of disturbance, calculate the anti-disturbance ability evaluation value through the preset attenuation function, if the evaluation value is lower than the set threshold, reset the equipment priority and reconstruct the logistics path of the process chain with the deepest propagation range and the most shared equipment, and generate a re-optimization scheme.

[0047] Digital twin simulation unit: import the re-optimization scheme into the digital twin, inject equipment running state and material arrival data, if load balancing or set rate does not meet the standard, dynamically adjust the constraint weight of QUBO model and iterate optimization, and simultaneously adjust the elastic coefficient of non-critical process time window according to the process execution progress.

[0048] In the manufacturing process of electric vehicle door handle accessories, the production chain involves multiple processes (such as stamping, welding, painting) and equipment nodes (such as stamping machines, welding robots, spraying equipment), and there is a complex dependency relationship between these processes and equipment, forming a dynamic production network. When the main plant proposes additional orders (i.e. insertion tasks) due to emergency events (such as product recall), traditional production scheduling often forcibly inserts through simple priority adjustment, leading to equipment resource conflicts, process dependency disorder, and even systemic instability of the production chain. To address this challenge, the present invention proposes an analytical method based on production chain scheduling dependency graph, aiming to accurately identify the impact range and conflict degree of insertion tasks on the production chain through structured analysis and quantitative means.

[0049] This part focuses on analyzing the production chain scheduling dependency graph, identifying the directly related equipment nodes and dependent process chains of insertion tasks, and using directed graph traversal algorithm to calculate the influence radius parameter and conflict intensity coefficient, providing key input data for subsequent quantum optimization and robustness evaluation, ensuring production stability when inserting emergency orders. The processing process of the analysis scheduling graph unit is as follows:

[0050] S1-1. Construct the production chain scheduling dependency graph:

[0051] The construction process of the production chain scheduling dependency graph is realized in the form of a directed graph, which is used to describe the dependency relationship between processes and equipment. Specifically, the node set consists of two types of nodes: process nodes and equipment nodes. Process nodes represent specific processes in the production chain, such as stamping, welding, painting, etc.; equipment nodes represent specific equipment in the production chain, such as stamping machines, welding robots, spraying equipment, etc. The edge set represents the dependency relationship between nodes, where the directed edge from the process node to the equipment node indicates that the process needs to use the equipment to execute, and the directed edge from one process node to another process node indicates that the latter process depends on the completion of the former process. The construction process relies on production process data and equipment allocation records, and generates a directed graph through automatic analysis, fully reflecting the topological structure of the production chain.

[0052] The directed graph is used to represent the dependency relationship between processes and devices, which can intuitively show the complex correlation in the production chain and facilitate the identification of the critical path and bottleneck link.

[0053] S1-2. Identify the device nodes directly associated with the single insertion task and the dependent process chain:

[0054] The single insertion task refers to an urgent order containing a series of process sets to be executed. The process of identifying the directly associated device nodes is to extract the device nodes related to the single insertion task from the production chain scheduling dependency graph, that is, to determine the specific devices required for each process in the single insertion task. The process of identifying the dependent process chain is to determine all the processes that each process in the single insertion task depends on by traversing the production chain scheduling dependency graph in reverse. The specific method is to start from the process node of the single insertion task, move in reverse along the directed edge, and gradually collect all reachable process nodes until all dependent paths are traversed to form a complete dependent process chain.

[0055] Identifying directly associated device nodes and dependent process chains can clearly define the direct impact range of the single insertion task on the production chain. By clearly defining the affected devices and processes, it provides a basis for evaluating the interference degree of the single insertion task on the existing schedule and avoids scheduling errors due to information loss.

[0056] S1-3. Use directed graph traversal algorithm to quantify the influence radius parameter and conflict intensity coefficient of the single insertion task:

[0057] Calculate the influence radius parameter:

[0058] The influence radius parameter is used to represent the maximum propagation depth of the processes and devices affected by the single insertion task in the production chain scheduling dependency graph. The calculation process starts from the process node of the single insertion task and proceeds forward along the directed edge, layer by layer accessing all affected nodes. Specifically, starting from each process node of the single insertion task, record the minimum number of edges required to reach each affected node, called the shortest path length. After traversal, count all the shortest path lengths and select the largest value as the influence radius parameter.

[0059] Calculate the conflict intensity coefficient:

[0060] The conflict intensity coefficient is used to represent the degree of conflict between the inserted task and the existing scheduled tasks in the use of devices. The calculation process first identifies a set of existing tasks that use the same device as the inserted task in the same time period, and then determines a set of devices that are shared by the inserted task and the existing tasks, referred to as a shared device set. For each device in the shared device set, the degree of time overlap of the inserted task and the existing task on the device is calculated. The specific method is to determine the execution time period of the inserted task and the existing task on the device, find the length of the time overlap of the two, and then divide the overlap length by the shorter of the two execution times to obtain the time overlap degree of the device. After calculating the time overlap degree of each device in the shared device set, the maximum time overlap degree is selected as the conflict intensity coefficient.

[0061] By calculating the influence radius parameter, the influence range of the inserted task can be quantified to help judge the degree of its impact on the overall production chain; by calculating the conflict intensity coefficient, the degree of resource competition between the inserted task and the existing scheduled tasks can be quantified. These quantitative indicators provide data support for optimization decisions, avoiding production chaos caused by blindly inserting urgent orders.

[0062] S1-4. Parameter storage:

[0063] The parameter storage process records the calculated influence radius parameter and conflict intensity coefficient as attributes of the inserted task, and records the affected node set, including all process nodes and device nodes affected by the inserted task. Specifically, the influence radius parameter, the conflict intensity coefficient, and the affected node set are stored in a structured manner in the system to ensure that these data can be directly called in subsequent steps.

[0064] The production chain scheduling dependency graph analysis method represents the dependency relationship between processes and devices by constructing a directed graph, identifies the directly associated device nodes and dependent process chains of the inserted task, and calculates the influence radius parameter and the conflict intensity coefficient using a traversal algorithm, thereby realizing the quantitative evaluation of the influence of the inserted task.

[0065] After analyzing the production chain scheduling dependency graph, identifying the directly associated device nodes and dependent process chains of the inserted task, and calculating the influence radius parameter and the conflict intensity coefficient, this part further constructs an optimization model and solves the device time period allocation and logistics path planning scheme to ensure the global optimality when the inserted task is inserted. The following is the detailed technical logic of converting the influence radius parameter and the conflict intensity coefficient into the coupling weight matrix of the QUBO model, and solving the global optimal solution of the device time period allocation and logistics path planning by the quantum annealing algorithm. The processing process of the conversion optimization solving unit is as follows:

[0066] S2-1. Convert the influence radius parameter and conflict intensity coefficient into the coupling weight matrix of the QUBO model:

[0067] The QUBO model, i.e., the quadratic unconstrained binary optimization model, is a method for describing optimization problems through binary variables and a coupling weight matrix, which is suitable for quantum annealing algorithm solving. This step uses the influence radius parameter and the conflict intensity coefficient to construct the QUBO model to quantify the constraint conditions and optimization objectives of the equipment time period allocation and the logistics path planning. The influence radius parameter represents the range of the impact of the insertion order task on the production chain, and the conflict intensity coefficient represents the conflict degree of the insertion order task and the existing scheduling task in the use of equipment.

[0068] Firstly, define the binary variables. For the equipment time period allocation, assign a binary variable to each process in each time period to represent whether the process uses a specific equipment in that time period, taking the value of 1 if it is used and 0 if it is not used. For the logistics path planning, assign a binary variable to each edge on the material transportation path to represent whether the material is transported from one node to another, taking the value of 1 if it is transported and 0 if it is not transported.

[0069] Secondly, construct the objective function. The objective function includes three parts: equipment conflict penalty, logistics cost, and insertion order task impact penalty. The equipment conflict penalty is used to avoid the same equipment being occupied by multiple processes in the same time period, and by setting a penalty term, it ensures that each equipment is only assigned to one process in each time period. The logistics cost is used to minimize the resource consumption of material transportation, and by setting a penalty term, it reflects the distance and resource usage of the transportation path. The insertion order task impact penalty is used to reduce the impact of the insertion order task on the production chain, and by setting a penalty term, it prioritizes the execution of insertion order processes according to the influence radius parameter and the conflict intensity coefficient, thereby reducing the impact range and conflict degree.

[0070] Then, construct the coupling weight matrix. The objective function of the QUBO model is composed of linear weights and quadratic weights of binary variables. Linear weights represent the independent influence of individual binary variables, and quadratic weights represent the interaction between binary variables. When converting the equipment conflict penalty into the coupling weight matrix, if two processes use the same equipment in the same time period, set a positive quadratic weight at the corresponding position in the matrix to increase the penalty strength. When converting the logistics cost into the coupling weight matrix, the resource consumption of the transportation path is directly taken as the linear weight of the corresponding binary variable. When converting the insertion order task impact penalty into the coupling weight matrix, set a negative linear weight for the binary variables of the insertion order task related processes to encourage priority execution, and increase the absolute value of the weight according to the influence radius parameter and adjust the weight intensity according to the conflict intensity coefficient.

[0071] The coupling weight matrix integrates all constraint conditions and optimization objectives as input data for the quantum annealing algorithm.

[0072] By employing the QUBO model, the problems of equipment time allocation and logistics route planning are unified into binary variable optimization. This not only transforms the problem into a simple optimization problem, but also enables the efficient solution of complex constraints and objectives through the quantum annealing algorithm. Integrating the influence radius parameter and conflict intensity coefficient into the coupling weight matrix ensures that the specific characteristics of the insertion task are fully considered in the optimization process. This approach minimizes interference with the existing schedule while meeting production demands, thereby improving the flexibility and stability of scheduling.

[0073] For ease of understanding, the following example is provided:

[0074] Define a binary variable:

[0075] Equipment time period allocation variable: definition , indicating process In time period Whether to use equipment , Indicates the first in the production chain A device. If used, then If not used, then .

[0076] Logistics route planning variables: definition This indicates whether the material has been delivered from the node. Transport to node If it involves transportation, then If not transported, then .

[0077] Construct the objective function :

[0078] objective function It includes three items: equipment conflict penalty, logistics cost penalty, and penalty for interrupting order tasks.

[0079] Device conflict penalty: same device In time period When a conflict occurs because the process is occupied by multiple processes, the penalty is as follows:

[0080] ;

[0081] This ensures that each piece of equipment is assigned to at most one process per time period.

[0082] Logistics costs: the cost of transporting materials, with penalties as follows:

[0083] ;

[0084] in Indicates from node To the node The transportation cost is calculated based on distance and resource consumption.

[0085] Penalties for interrupted tasks: Based on (The radius of influence parameter indicates the range of equipment and processes affected by the insert task) and (Conflict intensity coefficient, representing the degree of resource competition), the penalty term is:

[0086] ;

[0087] in and A preset positive coefficient is used to balance the weights of the scope of influence and the intensity of conflict.

[0088] Constructing the coupling weight matrix :

[0089] The objective function of the QUBO model is in the form of:

[0090] ;

[0091] in For binary variables (including) and ), For univariate weights, The coupling weights between variables.

[0092] Will , and Convert to matrix:

[0093] for If the process and In time period Using the same equipment ,but .

[0094] for .

[0095] for For the relevant processes of inserting orders ,exist Add to:

[0096] ;

[0097] in and A positive adjustment coefficient and a negative value encourage priority execution of inter-process orders to reduce impact and conflict.

[0098] S2-2. Solve the global optimal solution of equipment time slot allocation and logistics path planning in parallel through quantum annealing algorithm:

[0099] The quantum annealing algorithm uses the quantum tunneling effect to solve the optimal solution of the QUBO model. Based on the coupling weight matrix constructed in the previous steps, the equipment time slot allocation and logistics path planning scheme is optimized to realize parallel computation of the global optimal solution.

[0100] First, input and initialization. The coupling weight matrix is input to the quantum annealing machine, and the system is initialized to a quantum superposition state, representing the parallel exploration state of all possible solutions. The quantum superposition state allows simultaneous evaluation of multiple equipment time slot allocation and logistics path planning combinations.

[0101] Second, execute the solving process. The quantum annealing machine gradually reduces the transverse field strength, causing the system to gradually collapse from the quantum superposition state to the ground state. The ground state corresponds to the minimum value of the objective function, i.e., the optimized scheme of equipment time slot allocation and logistics path planning. During this process, the algorithm parallelly processes equipment time slot allocation variables and logistics path planning variables, explores all possible solution combinations, overcomes local extrema through quantum tunneling effect, and finally converges to the global optimal solution.

[0102] Then, output the calculation results. The quantum annealing machine returns a set of binary variable values, where the equipment time slot allocation scheme specifies the equipment usage of each process at each time slot, and the logistics path planning scheme determines the transportation path of the materials. This set of values minimizes the objective function while satisfying the constraints of equipment exclusivity, logistics path connectivity, and order insertion.

[0103] The quantum annealing algorithm can quickly converge to the global optimal solution by parallel exploration of the solution space, significantly improving the solving efficiency compared to traditional classical algorithms, especially for large-scale, high-dimensional optimization problems. The quantum tunneling effect overcomes local extrema, ensuring the global optimality of the optimization scheme, providing an efficient and reliable solution for equipment time slot allocation and logistics path planning.

[0104] By converting the influence radius parameter and conflict intensity coefficient into the coupling weight matrix of the QUBO model and using the quantum annealing algorithm to solve the equipment time slot allocation and logistics path planning scheme, global optimization is achieved when inserting the order task. The influence radius parameter and conflict intensity coefficient are accurately integrated into the optimization model, ensuring that the order task minimizes its impact on the production chain. The efficient parallel computing capability of the quantum annealing algorithm significantly improves the scheduling response speed and scheme quality. In the manufacturing of electric vehicle door handle accessories, it provides flexible and efficient technical support for the processing of urgent orders while maintaining the overall stability of the production chain.

[0105] The global optimal solution obtained by the quantum annealing algorithm provides an optimization basis for equipment time period allocation and logistics path planning. However, to further enhance the robustness of the production chain under the influence of the insertion task, it is necessary to analyze the coupling relationship and disturbance propagation characteristics between processes based on the global optimal solution, evaluate the disturbance resistance, and adjust the resource allocation and transportation scheme of the key process chain if necessary. This section focuses on the specific technical logic of this step to ensure the stability of the production chain in dynamic changes. The following is the technical logic of process chain topology relationship analysis and disturbance resistance evaluation based on the global optimal solution. The process of analyzing priority adjustment units is as follows:

[0106] S3-1. Extract the process chain topology relationship of the global optimal solution:

[0107] After obtaining the global optimal solution provided by the quantum annealing algorithm, extract the equipment time period allocation scheme and logistics path planning scheme contained in it to construct the process chain topology relationship graph. The process chain topology relationship graph is a directed graph, where nodes represent specific processes in the production chain, and edges represent the dependency relationship and device sharing relationship between processes. The dependency relationship edge reflects that the execution of one process must wait for the completion of another process, and the device sharing relationship edge indicates that two processes use the same device at the same time period. The construction process of the process chain topology relationship graph depends on the production process data and equipment time period allocation scheme, which is generated by automatically parsing production data.

[0108] S3-2. Analyze the shared device proportion and disturbance propagation range between processes:

[0109] Based on the process chain topology relationship graph, calculate the shared device proportion and disturbance propagation range between processes to quantify the impact of the insertion task on the production chain.

[0110] First, calculate the shared device proportion between processes. The shared device proportion is the ratio of the number of devices shared by two processes to the total number of devices used by each process. The specific calculation method is: count the number of devices used by both processes at least one time period as the numerator; count the total number of devices used by each process and remove the duplicate count as the denominator. The value of the shared device proportion ranges from 0 to 1, and the higher the value, the stronger the coupling between the two processes in terms of device resources.

[0111] Second, calculate the disturbance propagation range. The disturbance propagation range is determined by the breadth-first search algorithm, and the specific process is to start from a process, follow the dependency relationship edges and device sharing relationship edges in the process chain topology relationship graph, traverse all reachable process nodes, and count the number of these reachable processes. The number of reachable processes counted is the disturbance propagation range, and the larger the value, the more processes that can be affected by the disturbance of the process.

[0112] The calculation of the shared device proportion and the disturbance propagation range can quantify the coupling strength between processes and the influence range of the disturbance in the production chain, providing a key indicator for evaluating the stability of the production chain.

[0113] S3-3. Calculate the disturbance resistance capability evaluation value by a preset attenuation function:

[0114] Based on the shared device proportion and the disturbance propagation range, the disturbance resistance capability evaluation value of the process chain is calculated by a preset attenuation function to measure the stability of the production chain under disturbance.

[0115] The preset attenuation function is an exponential attenuation function, whose input is the topological distance between processes and whose output is an attenuation factor. The topological distance refers to the shortest path length between two processes in the process chain topological relationship graph, which is calculated by the Dijkstra algorithm. The preset attenuation function simulates the characteristic that the disturbance gradually weakens as the topological distance increases, i.e., the farther the distance, the smaller the impact of the disturbance.

[0116] The calculation of the disturbance resistance capability evaluation value is specific to a certain process chain. For each process in the process chain, all processes within its disturbance propagation range are traversed, and the product of the shared device proportion and the attenuation factor corresponding to each affected process is calculated. Then, all the products are added to obtain the disturbance resistance capability evaluation value of the process chain. The smaller the disturbance resistance capability evaluation value, the stronger the ability of the process chain to resist disturbance.

[0117] The calculation of the disturbance resistance capability evaluation value by the preset attenuation function can comprehensively consider the coupling strength between processes and the topological distance, accurately reflect the propagation and attenuation effect of the disturbance in the production chain, and serve as a quantitative indicator of the stability of the production chain.

[0118] S3-4. Determine and execute the reset device priority and reconfigure the logistics path:

[0119] According to the comparison of the disturbance resistance capability evaluation value and the preset threshold, the vulnerable process chain is identified and resource optimization measures are implemented to improve the stability of the production chain.

[0120] First, determine whether the disturbance resistance capability evaluation value of the process chain exceeds the preset threshold. If the disturbance resistance capability evaluation value exceeds the preset threshold, the process chain is determined to be a vulnerable process chain. The processes in the vulnerable process chain are easily affected by disturbance and need to be optimized first.

[0121] Second, identify the key processes in the vulnerable process chain. The key process refers to the process with the largest disturbance propagation range and the most shared devices. The number of shared devices is the total number of devices shared by the process and other processes. Optimization of the key process can minimize the impact of disturbance on the production chain.

[0122] Then, the equipment priority resetting is performed. For the processes in the fragile process chain, the unoccupied equipment is preferentially allocated to reduce the conflicts caused by equipment sharing. If there is no unoccupied equipment, the equipment with the shortest occupation time period is selected to reduce the intensity of resource competition. The updated equipment time period allocation scheme is recorded as part of the re-optimization scheme.

[0123] Then, the logistics path reconstruction is performed. For the material transportation paths involved in the fragile process chain, the path with the shortest transportation time is preferentially selected to speed up the material flow. If there is a conflict in the selected path, the transportation efficiency is ensured by adjusting to detour to low-load nodes. The updated logistics path planning scheme is also recorded as part of the re-optimization scheme.

[0124] Finally, the re-optimization scheme is updated. The reset equipment time period allocation scheme and the reconstructed logistics path planning scheme replace the corresponding parts in the global optimal solution to generate the final re-optimization scheme.

[0125] By identifying the fragile process chain through the anti-disturbance ability evaluation value and implementing targeted optimization, the stability of the production chain under the disturbance of the single insertion task can be effectively improved. The measures of equipment priority resetting and logistics path reconstruction reduce resource conflicts and transportation delays, ensuring the continuity and efficiency of the production chain.

[0126] The present application realizes the quantitative evaluation and optimization of the stability of the production chain by constructing the process chain topological relationship diagram, analyzing the shared equipment proportion and disturbance propagation range between processes, using a preset attenuation function to calculate the anti-disturbance ability evaluation value, and implementing equipment priority resetting and logistics path reconstruction for the key process chain when the anti-disturbance ability evaluation value exceeds the preset threshold. The calculation of the shared equipment proportion and the disturbance propagation range accurately identifies the fragile links in the production chain, the anti-disturbance ability evaluation value provides a quantitative standard for stability, and the resource optimization measures significantly improve the robustness of the production chain. This method provides scientific and effective technical support for coping with the single insertion task disturbance in the manufacturing of door handle accessories for electric vehicles, ensuring the stable operation of the production chain.

[0127] The previous step has identified the device nodes and dependent process chains associated with the insertion task by analyzing the production chain scheduling dependency graph, and has quantified the influence radius parameter and conflict intensity coefficient using a directed graph traversal algorithm. Subsequently, based on the quantum annealing algorithm, the QUBO model is solved to generate the global optimal solution of device time period allocation and logistics path planning, and the anti-disturbance capability is analyzed through process chain topological relationship to complete the preliminary design of the re-optimization scheme. However, in order to verify the feasibility and stability of the scheme in actual production, it needs to be imported into the digital twin, combined with real-time data for dynamic adjustment and iterative optimization, to realize the collaborative operation of insertion task and steady-state scheduling. This part focuses on the specific technical logic of this step to ensure the robustness and efficiency of the production chain under disturbance. The following is the specific technical logic of importing the re-optimization scheme into the digital twin and realizing the collaborative operation of insertion task and steady-state scheduling through dynamic adjustment. The processing process of the digital twin simulation unit is as follows:

[0128] S4-1. Import the re-optimization scheme into the digital twin and inject real-time data:

[0129] The re-optimization scheme includes device time period allocation scheme and logistics path planning scheme, which needs to be mapped to the digital twin to simulate the actual running state of the production chain. The device time period allocation scheme specifies the device occupancy of each process in each time period, and the logistics path planning scheme determines the transportation path of the material.

[0130] Firstly, input the device time period allocation scheme into the device scheduling module of the digital twin to generate the time allocation table of virtual devices, which reflects the occupancy state of each device in different time periods; at the same time, input the logistics path planning scheme into the material transportation module of the digital twin to generate the path planning of virtual logistics network, which reflects the transportation route of the material. Secondly, collect the real-time data of the device running state through the sensor, including load, fault and maintenance state, and inject these data into the digital twin; collect the real-time position and transportation progress data of the material through the logistics tracking system, and also inject these data into the digital twin. The digital twin combines the re-optimization scheme and the real-time injected data to generate the real-time running simulation of the production chain as the basis for subsequent evaluation.

[0131] S4-2. Evaluate load balancing and perfect rate:

[0132] Based on the simulation results of the digital twin, calculate the load balancing index and perfect rate index to judge whether the running state of the production chain meets the preset requirements.

[0133] First, evaluate load balancing. Load balancing indicators are determined by analyzing the load rate of each device and the degree of dispersion of its distribution. The specific method is: calculate the load rate of each device, that is, the proportion of time occupied by the device in the total available time; then, calculate the cubic deviation of all device load rates, which highlights the imbalance of load distribution. The calculation process of cubic deviation is: first, calculate the deviation of each device load rate from the average load rate, then add the cubic value of all deviations, and finally take the average. If the load balancing indicator exceeds the preset threshold, for example, the standard value is set to 0.05, it indicates that the device load distribution is uneven, and the load balancing requirement is not met.

[0134] Second, evaluate the rate of completion. The rate of completion is the ratio of the number of processes completed on time to the total number of processes. The completion status of the process is determined by comparing the actual arrival time of the material with the planned time: if the material arrives before or at the planned time, the process is considered complete; if the material arrives later than the planned time, the process is considered not complete. When calculating the completion rate, divide the number of all complete processes by the total number of processes. If the completion rate is lower than the preset threshold, for example, the standard value is set to 0.95, it indicates that the material supply cannot fully meet the production demand, and the completion rate requirement is not met.

[0135] Load balancing and completion rate are core indicators to measure the stability and efficiency of the production chain. Load balancing evaluation can identify bottlenecks in device resource allocation, such as excessive occupation or idling of a device; completion rate evaluation can reveal the lack of material supply, such as production interruption caused by transportation delay. Through the simulation evaluation of digital twins, problems can be quickly found and located without interfering with actual production, providing a clear direction for subsequent optimization.

[0136] S4-3. Dynamically adjust the constraint weight of the QUBO model and iterate optimization:

[0137] If the load balancing indicator or the completion rate indicator does not meet the preset requirements, the constraint weight of the QUBO model needs to be adjusted and re-optimized to generate a better device time period allocation scheme and logistics path planning scheme.

[0138] First, adjust the constraint weight according to the evaluation results. If the load balancing indicator does not meet the standard, increase the weight of the device conflict penalty to strengthen the constraint on device resource exclusivity. The specific adjustment method is to multiply the original device conflict penalty weight by a coefficient greater than 1, the size of the coefficient is proportional to the degree of over-standard of the load balancing indicator, for example, the more over-standard, the larger the coefficient. If the completion rate indicator does not meet the standard, increase the weight of the logistics cost to strengthen the optimization of material transportation efficiency. The specific adjustment method is to multiply the original logistics cost weight by a coefficient greater than 1, the size of the coefficient is proportional to the degree of deficiency of the completion rate indicator, for example, the more deficient, the larger the coefficient.

[0139] Secondly, based on the adjusted constraint weights, the coupling weight matrix of the QUBO model is reconstructed. The coupling weight matrix is used to describe the mutual influence between device occupation and logistics path, and the reconstruction process is to recalculate the numerical value of each element in the matrix according to the new weight. Subsequently, the updated coupling weight matrix is input into the quantum annealing algorithm to generate a new device time period allocation scheme and logistics path planning scheme by algorithm solving. Then, the newly generated scheme is imported into the digital twin, and the aforementioned real-time data injection and evaluation steps are repeated until the load balancing index and the full set rate index meet the preset requirements.

[0140] Dynamic adjustment of the constraint weights of the QUBO model can flexibly optimize device resource allocation and logistics path arrangement according to the actual running state of the production chain. The iterative optimization process ensures continuous improvement of the scheduling scheme through repeated adjustment and verification, ultimately meeting production requirements. This method improves the adaptability and stability of the production chain to disturbances, such as maintaining efficient operation when introducing single insertion tasks.

[0141] S4-4. Fuzzy adjustment of non-critical process time window elasticity coefficient:

[0142] In the iterative optimization process, the time window elasticity coefficient of non-critical processes is adjusted to improve the flexibility of the production chain.

[0143] The time window elasticity coefficient is a parameter used to control the flexibility of process scheduling, representing the range of adjustment of the process start time within the preset time range to adapt to dynamic changes caused by the introduction of single insertion tasks or other disturbances in the production chain. The time window elasticity coefficient is determined by analyzing the process slack time and real-time execution progress deviation. Specifically, first, calculate the process slack time, which is the difference between the latest start time and the earliest start time of the process minus the processing time. Processes with a slack time greater than zero are identified as non-critical processes; then, based on the deviation between the actual start time and the planned start time of the process, a fuzzy logic controller is used to generate an adjustment amount. When the deviation is positive, increase the elasticity coefficient to expand the adjustment range, and when the deviation is negative, reduce the elasticity coefficient to limit the adjustment range; finally, add the original time window elasticity coefficient and the adjustment amount to get the updated time window elasticity coefficient. The introduction and dynamic adjustment of this parameter ensure that non-critical processes have flexible time adjustment capabilities without affecting the overall progress of the production chain, thereby improving the adaptability and stability of the production chain to sudden disturbances and ensuring the coordinated operation of single insertion tasks and steady-state scheduling.

[0144] Firstly, non-critical processes are identified. Non-critical processes refer to processes with certain slack time in the production chain, that is, the difference between the earliest start time and the latest start time of the process is greater than the processing time. The calculation method of slack time is to determine the earliest start time and the latest start time of the process, then calculate the difference between the two, and compare it with the processing time. If the difference is greater than the processing time, the process is a non-critical process. The existence of slack time provides adjustment space for process scheduling.

[0145] Secondly, the fuzzy logic controller is used to adjust the time window flexibility coefficient. The fuzzy logic controller takes the deviation of process execution progress as input and outputs the adjustment amount of time window flexibility coefficient. The calculation method of process execution progress deviation is to divide the difference between actual start time and planned start time by processing time to get a ratio. If the process is executed in advance, for example, the actual start time is earlier than the planned start time, the time window flexibility coefficient should be appropriately reduced to limit the adjustment range; if the process is delayed, for example, the actual start time is later than the planned start time, the time window flexibility coefficient should be appropriately increased to provide more adjustment space.

[0146] Finally, the adjusted time window flexibility coefficient is applied to update the start time adjustment range of non-critical processes. The specific method is to recalculate the allowed range between the earliest start time and the latest start time of non-critical processes according to the new time window flexibility coefficient, and record the adjusted scheme as part of the re-optimization scheme.

[0147] Fuzzy adjustment of non-critical process time window flexibility coefficient can improve the adaptability of production chain to disturbance without affecting the critical path. For example, when a single task is introduced, the time adjustment of non-critical process can avoid impacting the overall progress. Through dynamic adjustment of time window, the production chain can maintain flexibility under external disturbance, ensuring the stability and efficiency of overall scheduling.

[0148] The present application realizes accurate simulation of production chain running state by importing re-optimization scheme into digital twin and injecting real-time data. Based on the simulation results, load balancing index and perfect fit rate index are evaluated. If the preset requirements are not met, the QUBO model constraint weight is dynamically adjusted and iteratively optimized, and the time window flexibility coefficient of non-critical process is adjusted by fuzzy logic to ensure the flexibility and stability of production chain under single task disturbance. The real-time simulation and dynamic adjustment mechanism of digital twin significantly improves the robustness and response ability of production chain, providing efficient and reliable technical support for the manufacturing of electric vehicle door handle accessories.

[0149] The above formulas are dimensionless values calculated, and the formulas are obtained by collecting a large amount of data to simulate the most recent real situation. The preset parameters in the formula are set by the person skilled in the art according to the actual situation.

[0150] It should be noted that the system of the present application can be deployed in the device itself to realize embedded application, or can be run on PC or other terminal with user interface, so as to meet various hardware environment and use requirements.

[0151] The above only describes certain exemplary embodiments of the present application by way of illustration, and it is needless to say that the described embodiments can be modified in various ways without departing from the spirit and scope of the present application by those skilled in the art. Therefore, the above figures and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present application.

[0152] It should be noted that in this document, relational terms such as first and second and the like can merely be used to distinguish one entity or action from another, without necessarily requiring or implying that there is any such actual relationship or order between or among the entities or actions. Moreover, the terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by "comprises... a" does not, without more constraints, exclude the existence of additional identical elements in the process, method, article, or apparatus that comprises the element.

[0153] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto, and any skilled person in the art can easily think of changes or replacements within the technical range disclosed by the present application, which should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A manufacturing management system for door handle components for electric vehicles, characterized in that, include: The system includes a scheduling graph analysis unit, a transformation optimization solution unit, an analysis priority adjustment unit, and a digital twin simulation unit. The scheduling graph analysis unit analyzes the production chain scheduling dependency graph, identifies the equipment nodes and dependent process chains directly associated with the insertion task, and uses a directed graph traversal algorithm to quantify the influence radius parameter and conflict intensity coefficient of the insertion task. Among them, the influence radius parameter is the maximum propagation depth of the processes and equipment affected by the insertion task in the production chain scheduling dependency graph; the conflict intensity coefficient is the maximum time overlap between the insertion task and the existing scheduled task in terms of equipment usage. Transformation optimization solution unit: The influence radius parameter and conflict intensity coefficient are transformed into the coupling weight matrix of the QUBO model, and the global optimal solution of the equipment time allocation scheme and logistics route planning scheme is solved in parallel by the quantum annealing algorithm; Analysis Priority Adjustment Unit: Based on the process chain topology relationship of the global optimal solution, analyze the proportion of shared equipment between processes and the disturbance propagation range, calculate the anti-disturbance capability assessment value through a preset attenuation function, and if the assessment value is lower than the set threshold, reset the equipment priority and reconstruct the logistics path for the process chain with the deepest propagation range and the most shared equipment, and generate a re-optimization scheme; After obtaining the global optimal solution provided by the quantum annealing algorithm, the equipment time allocation scheme and logistics path planning scheme contained therein are extracted to construct a process chain topology graph, where nodes represent specific processes in the production chain, and edges represent the dependencies and equipment sharing relationships between processes; dependency edges reflect that the execution of one process must wait for the completion of another process, and equipment sharing edges indicate that two processes use the same equipment at the same time within a certain period of time; Based on the dependency edges and shared equipment edges in the process chain topology graph, calculate the shared equipment ratio for each pair of processes. The shared equipment ratio is defined as the ratio of the number of shared equipment between two processes to the total number of equipment used by each process. Starting from each process, the breadth-first search algorithm is used to traverse the reachable process nodes in the process chain topology graph to determine the disturbance propagation range of each process. The disturbance propagation range is defined as the number of reachable processes. The disturbance resistance assessment value of the process chain is calculated using a preset attenuation function, which is an exponential attenuation function based on the topological distance between processes. The disturbance resistance assessment value is defined as the sum of the products of the proportion of shared equipment of each process in the process chain to the processes within its disturbance propagation range and the attenuation factor. When the anti-disturbance capability assessment value is lower than the preset threshold, identify the process chain with the largest disturbance propagation range and the largest number of shared equipment, perform equipment priority reset and logistics path reconstruction on it, replace the corresponding part in the global optimal solution with the reset equipment time allocation scheme and the reconstructed logistics path planning scheme, and generate a re-optimization scheme. Digital twin simulation unit: The re-optimization scheme is imported into the digital twin, and the equipment operation status and material arrival data are injected. If the load balance or kitting rate does not meet the standard, the constraint weights of the QUBO model are dynamically adjusted and iteratively optimized. At the same time, the time window elasticity coefficient of non-critical processes is fuzzily adjusted according to the process execution progress.

2. The manufacturing and management system for electric vehicle door handle accessories according to claim 1, characterized in that, The processing procedure for parsing the scheduling graph unit is as follows: A production chain scheduling dependency graph is constructed. This graph is a directed graph with node sets including process nodes and equipment nodes, and edge sets representing the usage relationships between processes and equipment, as well as the dependencies between processes. Equipment nodes related to the insertion task processes are extracted from the graph, identifying the equipment nodes directly associated with the insertion task. The graph is then traversed in reverse to determine all processes that the insertion task depends on, identifying the dependency chain of the insertion task. A directed graph traversal algorithm is used to calculate the influence radius parameter of the insertion task and the conflict intensity coefficient of the insertion task.

3. The manufacturing and management system for electric vehicle door handle accessories according to claim 2, characterized in that, The process of transforming and optimizing the solution element is as follows: The influence radius parameter and conflict intensity coefficient are associated with the binary variables of equipment time allocation and logistics route planning, respectively, to calculate the degree of interference of the insertion task on the existing production plan. The direct effect of the influence radius parameter on a single binary variable is represented by linear weights, and the coupling effect of the conflict intensity coefficient between binary variables is represented by quadratic weights, forming a coupling weight matrix. The coupling weight matrix is ​​input into the quantum annealing machine, and the global optimal solution of the equipment time allocation scheme and the logistics route planning scheme is solved in parallel using the quantum annealing algorithm.

4. The manufacturing and management system for electric vehicle door handle accessories according to claim 3, characterized in that, The process of transforming and optimizing the solution unit also includes include: Among them, the equipment time allocation scheme determines the occupancy status of each piece of equipment in each time period, and the logistics route planning scheme determines the optimal route for material transportation.

5. The manufacturing and management system for electric vehicle door handle accessories according to claim 1, characterized in that, The analysis of the priority adjustment unit's processing includes: Equipment priority reset is defined as prioritizing the allocation of equipment that is not occupied by other processes or that occupies the least amount of time. Logistics route reconstruction is defined as selecting the route with the shortest transportation time.

6. The manufacturing and management system for electric vehicle door handle accessories according to claim 5, characterized in that, The processing procedure of the digital twin simulation unit is as follows: Step 1: Map the equipment time allocation scheme and logistics route planning scheme to the equipment scheduling module and material transportation module of the digital twin to generate a time allocation table for virtual equipment and a route plan for the virtual logistics network; collect various data of the equipment through sensors and logistics tracking system, and inject them into the digital twin to simulate the real-time operation of the production chain; Step 2: Calculate the equipment load rate and availability rate based on the simulation results, and determine whether the load balancing index and availability rate index meet the preset requirements. Step 3: If the load balancing index or the completeness rate index does not meet the preset requirements, adjust the equipment conflict penalty weight and logistics cost weight of the QUBO model, reconstruct the coupling weight matrix, solve the new equipment time slot allocation scheme and logistics route planning scheme through the quantum annealing algorithm, and import the digital twin for iterative optimization. Step 4: Simultaneously identify non-critical processes based on the process execution progress, and adjust the time window elasticity coefficient of non-critical processes through a fuzzy logic controller. Repeat steps one through four until both the load balancing metric and the kitting rate metric meet the preset requirements.

7. The manufacturing and management system for electric vehicle door handle components according to claim 6, characterized in that, The processing steps of the digital twin simulation unit also include: Non-critical processes refer to processes in the production chain that have a certain amount of slack time. That is, the difference between the earliest start time and the latest start time of the process is greater than the processing time of the process. The slack time is calculated by first determining the earliest start time and the latest start time of the process, then calculating the difference between the two, and comparing it with the processing time. If the difference is greater than the processing time, the corresponding process is a non-critical process.

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